The existing research on robust Graph Neural Networks (GNNs) fails to acknowledge the significance of directed graphs in providing rich information about networks' inherent structure. This work presents the first investigation into the robustness of GNNs in the context of directed graphs, aiming to harness the profound trust implications offered by directed graphs to bolster the robustness and resilience of GNNs. Our study reveals that existing directed GNNs are not adversarially robust. In pursuit of our goal, we introduce a new and realistic directed graph attack setting and propose an innovative, universal, and efficient message-passing framework as a plug-in layer to significantly enhance the robustness of GNNs. Combined with existing defense strategies, this framework achieves outstanding clean accuracy and state-of-the-art robust performance, offering superior defense against both transfer and adaptive attacks. The findings in this study reveal a novel and promising direction for this crucial research area. The code will be made publicly available upon the acceptance of this work.

翻译：现有关于鲁棒图神经网络（GNNs）的研究未能充分认识到有向图在提供网络内在结构丰富信息方面的重要性。本文首次探讨了有向图场景下GNNs的鲁棒性问题，旨在利用有向图所蕴含的深层信任信息来增强GNNs的鲁棒性与韧性。我们的研究揭示，现有有向GNNs并不具备对抗鲁棒性。为实现目标，我们提出了一种新颖且现实的有向图攻击设定，并设计了一种创新、通用且高效的消息传递框架作为插件层，以显著提升GNNs的鲁棒性。该框架结合现有防御策略，在干净准确率和最先进的鲁棒性能方面均取得卓越表现，能够有效防御迁移攻击与自适应攻击。本研究的发现为该关键研究领域揭示了一个新颖且富有前景的方向。代码将在本工作被接收后公开提供。